AI-Based Material Selection Evaluation for Heavy Construction Machinery Components

Abstract

Selecting the right material for heavy construction machinery components is a crucial step in ensuring operational performance, efficiency, and sustainability. Artificial intelligence (AI) technology offers a new approach to this selection process with its fast and high-precision data analysis capabilities. This study aims to evaluate the effectiveness of AI implementation in material selection for heavy construction machinery components. The study utilized machine learning algorithms, such as random forest and artificial neural networks, to analyze material parameters including strength, wear resistance, density, and production cost. The results showed that the AI-based method can improve material selection efficiency by up to 35% compared to conventional methods. In addition, this method is also able to reduce selection errors that often occur in manual approaches. By utilizing AI, the selection process becomes faster, more accurate, and more sustainable, supporting the development of more modern and environmentally friendly construction technologies.